Raman Multi-Omic Snapshots of Koshihikari Rice Kernels Reveal Important Molecular Diversities with Potential Benefits in Healthcare

This study exploits quantitative algorithms of Raman spectroscopy to assess, at the molecular scale, the nutritional quality of individual kernels of the Japanese short-grain rice cultivar Koshihikari in terms of amylose-to-amylopectin ratio, fractions of phenylalanine and tryptophan aromatic amino acid residues, protein-to-carbohydrate ratio, and fractions of protein secondary structures. Statistical assessments on a large number of rice kernels reveal wide distributions of the above nutritional parameters over nominally homogeneous kernel batches. This demonstrates that genetic classifications cannot catch omic fluctuations, which are strongly influenced by a number of extrinsic factors, including the location of individual grass plants within the same rice field and the level of kernel maturation. The possibility of collecting nearly real-time Raman "multi-omic snapshots" of individual rice kernels allows for the automatic (low-cost) differentiation of groups of kernels with restricted nutritional characteristics that could be used in the formulation of functional foods for specific diseases and in positively modulating the intestinal microbiota for protection against bacterial infection and cancer prevention.

Sequence Control in Asymmetric Terpolymerizations of meso-Epoxides, CO2, and Phthalic Anhydride: Unprecedented Statistical Ester-Carbonate Distributions

The statistical terpolymerization of epoxides, CO2 and cyclic anhydrides remains challenging, mainly because epoxide/CO2 and epoxide/anhydride copolymerizations typically proceed at considerably different rates. Herein, we report the syntheses of novel chiral terpolymers with unprecedented statistical distributions of carbonate and ester units (up to 50% junction units) via the one-pot reaction of cyclohexene oxide, phthalic anhydride, and CO2 under mild conditions using enantiopure bimetallic aluminum-complex-based catalyst systems. Notably, all resulting terpolymers exhibited excellent enantioselectivities (≥96% ee) that were independent of the carbonate-ester distribution. The statistical compositions of the carbonate and ester units in the resulting terpolymers were determined via 1H and 13C NMR spectroscopies. Furthermore, thermal properties were tuned by altering the ester content of the chiral terpolymer without influencing the enantioselective ring-opening step involving the meso-epoxide. This asymmetric terpolymerization methodology is also compatible with a variety of meso-epoxides to afford the corresponding terpolymers with 17%-25% junction units and excellent enantioselectivities (94%-99% ee). The present study is expected to provide new guidelines for preparing a broad range of biodegradable polymers with excellent enantioselectivities and adjustable properties.

Power Law for Estimating Underdetection of Foodborne Disease Outbreaks, United States

We fit a power law distribution to US foodborne disease outbreaks to assess underdetection and underreporting. We predicted that 788 fewer than expected small outbreaks were identified annually during 1998-2017 and 365 fewer during 2018-2019, after whole-genome sequencing was implemented. Power law can help assess effectiveness of public health interventions.

Assessment of genotyping markers in the molecular characterization of a population of clinical isolates of Fusarium in Colombia

INTRODUCTION

Fusarium is a very heterogeneous group of fungi, difficult to classify, with a wide range of living styles, acting as saprophytes, parasites of plants, or pathogens for humans and animals. Prevalence of clinical fusariosis and lack of effective treatments have increased the interest in the precise diagnosis, which implies a molecular characterization of Fusarium populations.

OBJECTIVE

We compared different genotyping markers in their assessment of the genetic variability and molecular identification of clinical isolates of Fusarium.

MATERIALS AND METHODS

We evaluated the performance of the fingerprinting produced by two random primers: M13, which amplifies a minisatellite sequence, and (GACA)4, which corresponds to a simple repetitive DNA sequence. Using the Hunter Gaston Discriminatory Index (HGDI), an analysis of molecular variance (AMOVA), and a Mantel test, the resolution of these markers was compared to the reference sequencing-based and PCR genotyping methods.

RESULTS

The highest HGDI value was associated with the M13 marker followed by (GACA)4. AMOVA and the Mantel tests supported a strong correlation between the M13 classification and the reference method given by the partial sequencing of the transcription elongation factor 1-alpha (TEF1-α) and rDNA 28S.

CONCLUSION

The strong correlation between the M13 classification and the sequencingbased reference together with its higher resolution demonstrates its adequacy for the characterization of Fusarium populations.

Soft Compression for Lossless Image Coding Based on Shape Recognition

Soft compression is a lossless image compression method that is committed to eliminating coding redundancy and spatial redundancy simultaneously. To do so, it adopts shapes to encode an image. In this paper, we propose a compressible indicator function with regard to images, which gives a threshold of the average number of bits required to represent a location and can be used for illustrating the working principle. We investigate and analyze soft compression for binary image, gray image and multi-component image with specific algorithms and compressible indicator value. In terms of compression ratio, the soft compression algorithm outperforms the popular classical standards PNG and JPEG2000 in lossless image compression. It is expected that the bandwidth and storage space needed when transmitting and storing the same kind of images (such as medical images) can be greatly reduced with applying soft compression.

Comparing Continuous and Binary Group-based Trajectory Modeling Using Statin Medication Adherence Data

BACKGROUND

Of 58 medication adherence group-based trajectory modeling (GBTM) published studies, 74% used binary and 26% used continuous GBTM. Few studies provided a rationale for this choice. No medication adherence studies have compared continuous and binary GBTM.

OBJECTIVE

The objective of this study was to assess whether continuous versus binary GBTM: (1) impacts adherence trajectory shapes; and (2) results in the differential classification of patients into adherence groups.

METHODS

Patients were prevalent statin users with myocardial infarction hospitalization, 66+ years old, and continuously enrolled in fee-for-service Medicare. Statin medication adherence was measured 6 months prehospitalization using administrative claims. Final GBTM specifications beyond default settings were selected using a previously defined standardized procedure and applied separately to continuous and binary (proportion of days covered ≥0.80) medication adherence measures. Assignment to adherence groups was compared between continuous and binary models using percent agreement of patient classification and the κ coefficient.

RESULTS

Among 113,296 prevalent statin users, 4 adherence groups were identified in both models. Three groups were consistent: persistently adherent, progressively nonadherent, and persistently nonadherent. The fourth continuous group was moderately adherent (progressively adherent in the binary model). When comparing patient assignment into adherence groups between continuous and binary trajectory models, only 78.4% of patients were categorized into comparable groups (κ=0.641; 95% confidence interval: 0.638-0.645). The agreement was highest in the persistently adherent group (∼94%).

CONCLUSIONS

Continuous and binary trajectory models are conceptually different measures of medication adherence. The choice between these approaches should be guided by study objectives and the role of medication adherence within the study-exposure, outcome, or confounder.

Weibull Modeling of Controlled Drug Release from Ag-PMA Nanosystems

Traditional pharmacotherapy suffers from multiple drawbacks that hamper patient treatment such as antibiotic resistances or low drug selectivity and toxicity during systemic applications. Some functional hybrid nanomaterials are designed to handle the drug release process under remote-control. More attention has recently been paid to synthetic polyelectrolytes for their intrinsic properties which allow them to rearrange into compact structures, ideal to be used as drug carriers or probes influencing biochemical processes. The presence of Ag nanoparticles (NPs) in the Poly methyl acrylate (PMA) matrix leads to an enhancement of drug release efficiency, even using a low-power laser whose wavelength is far from the Ag Surface Plasmon Resonance (SPR) peak. Further, compared to the colloids, the nanofiber-based drug delivery system has shown shorter response time and more precise control over the release rate. The efficiency and timing of involved drug release mechanisms has been estimated by the Weibull distribution function, whose parameters indicate that the release mechanism of nanofibers obeys Fick's first law while a non-Fickian character controlled by diffusion and relaxation of polymer chains occurs in the colloidal phase.

Choosing Interim Sample Sizes in Group Sequential Designs

This manuscript investigates sample sizes for interim analyses in group sequential designs. Traditional group sequential designs (GSD) rely on "information fraction" arguments to define the interim sample sizes. Then, interim maximum likelihood estimators (MLEs) are used to decide whether to stop early or continue the data collection until the next interim analysis. The possibility of early stopping changes the distribution of interim and final MLEs: possible interim decisions on trial stopping excludes some sample space elements. At each interim analysis the distribution of an interim MLE is a mixture of truncated and untruncated distributions. The distributional form of an MLE becomes more and more complicated with each additional interim analysis. Test statistics that are asymptotically normal without a possibly of early stopping, become mixtures of truncated normal distributions under local alternatives. Stage-specific information ratios are equivalent to sample size ratios for independent and identically distributed data. This equivalence is used to justify interim sample sizes in GSDs. Because stage-specific information ratios derived from normally distributed data differ from those derived from non-normally distributed data, the former equivalence is invalid when there is a possibility of early stopping. Tarima and Flournoy [3] have proposed a new GSD where interim sample sizes are determined by a pre-defined sequence of ordered alternative hypotheses, and the calculation of information fractions is not needed. This innovation allows researchers to prescribe interim analyses based on desired power properties. This work compares interim power properties of a classical one-sided three stage Pocock design with a one-sided three stage design driven by three ordered alternatives.

Benchmarking of public health surveillance of COVID-19 in Colombia: First semester

Introduction: Public health surveillance together with good sanitary decisions is essential for the proper management of the SARS-CoV-2 pandemic. Objective: To compare the performance of Colombian departments based on the quality of the data and to build the national ranking. Materials and methods: We analyzed the accumulated cases published between March 6 and September 1, 2020, by the Instituto Nacional de Salud. To achieve comparability, the analyses considered the day the first case was diagnosed as the first analysis date for each department. The fulfillment of Benford’s law was assessed with p-values in the log-likelihood ratio or chi-square tests. The analysis was completed with the lethality observed in each department and then the performance ranking was established. Results: Bogotá and Valle del Cauca had optimal public health surveillance performance all along. The data suggest that Antioquia, Nariño, and Tolima had good containment and adequate public health surveillance after the economic opening beginning on June 1, 2020. Conclusion: We obtained the ranking of the departments regarding the quality of public health surveillance data. The best five departments can be case studies to identify the elements associated with good performance.

Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data

PURPOSE

The rationale for choosing a final group-based trajectory modeling (GBTM) specification and evaluations of patient adherence patterns within groups are often omitted in the GBTM medication adherence literature. We aimed to (1) reveal the complexity of GBTM and (2) assess model discrimination of patient medication adherence patterns.

METHODS

Medicare administrative claims were used to measure statin medication adherence as a continuous value in the 6 months before an acute myocardial infarction (AMI) hospitalization. Different GBTM specifications beyond default settings were constructed and compared with the Bayesian information criterion. Spaghetti plots were used to compare individual adherence patterns with group averages.

RESULTS

Overall, 113,296 prevalent statin users met eligibility criteria. Four adherence groups were identified: persistently adherent, moderately adherent, progressively nonadherent, and persistently nonadherent. Spaghetti plots showed the persistently adherent and persistently nonadherent groups had relatively homogeneous adherence patterns that matched predicted trajectories well. Spaghetti plots also showed that, while adherence patterns in the progressively nonadherent group were not as homogeneous, most patients in this group appeared to be discontinuing statin therapy pre-AMI.

CONCLUSIONS

Subjective decisions are necessary to identify a final trajectory model. Greater transparency and disclosure of these decisions in the medication adherence literature are needed. Individual patient adherence patterns from spaghetti plots provided additional diagnostic information about trajectory models beyond standard model-fit assessments to determine if group-average adherence estimates represent homogeneous patterns of medication adherence.

Cancer Survival Data Representation for Improved Parametric and Dynamic Lifetime Analysis

Survival functions are often characterized by a median survival time or a 5-year survival. Whether or not such representation is sufficient depends on tumour development. Different tumour stages have different mean survival times after therapy. The validity of an exponential decay and the origins of deviations are substantiated. The paper shows, that representation of survival data as logarithmic functions visualizes differences better, which allows for differentiating short- and long-term dynamic lifetime. It is more instructive to represent the changing lifetime expectancy for an individual who has survived a certain time, which can be significantly different from the initial expectation just after treatment. Survival data from 15 publications on cancer are compared and re-analysed based on the well-established: (i) exponential decay (ii) piecewise constant hazard (iii) Weibull model and our proposed parametric survival models, (iv) the two-τ and (v) the sliding-τ model. The new models describe either accelerated aging or filtering out of defects with numerical parameters with a physical meaning and add information to the usually provided log-rank P-value or median survival. The statistical inhomogeneity in a group by mixing up different tumour stages, metastases and treatments is the main origin for deviations from the exponential decay.

A method for multiparametric statistical quantification of the heterogeneity of free Na+ concentration by 19 F MR spectroscopy: Proof of principle in silico and in vitro

Sodium(I) (Na⁺ ) is one of the most important cations in mammalian tissues. Since Na⁺ plays a key role in basic cell function, noninvasive methods for measuring intracellular concentrations of free sodium ions in biological tissue have been developed on the basis of ¹⁹ F NMR spectroscopy. However, intracellular Na⁺ levels are often not uniform throughout a tissue volume (or voxel) being measured. In such cases, [Na⁺ ] heterogeneity is not reflected in results obtained by the classical technique, and may even result in biased average values. For this reason, we have designed an approach for quantifying [Na⁺ ] heterogeneity. First, the ¹⁹ F MRS resonance from FCrown-1 serving as a "Na⁺ probe" is transformed into a [Na⁺ ] curve. Then the digital points of the resulting [Na⁺ ] profile are used to construct a histogram with specially developed algorithms. From each [Na⁺ ] histogram, at least eight quantitative parameters describing the underlying statistical [Na⁺ ] distribution were computed: weighted median, weighted mean, standard deviation, range, mode(s), kurtosis, skewness, and entropy. In addition to our new paradigm, we present a first validation based on (i) computer simulations and (ii) experimentally obtained ¹⁹ F MR spectra of model solutions. This basic proof of principle warrants future in vivo experiments, in particular because of its ability to provide quantitative information complementary to that made available by commonly used ²³ Na MRI: (i) multiparametric statistical characterization of [Na⁺ ] distributions; (ii) total [Na⁺ ] heterogeneity analysis not intrinsically limited by the size of any MRI voxels; and (iii) analysis of unequivocally intracellular [Na⁺ ], as opposed to measurement of a combination of intra- and extracellular [Na⁺ ].

Multivariate Pointwise Information-Driven Data Sampling and Visualization

With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

Prevalence Estimates for Pharmacological Neuroenhancement in Austrian University Students: Its Relation to Health-Related Risk Attitude and the Framing Effect of Caffeine Tablets

Background: Pharmacological neuroenhancement (PN) is defined as the use of illicit or prescription drugs by healthy individuals for cognitive-enhancing purposes. The present study aimed (i) to investigate whether including caffeine tablets in the definition of PN within a questionnaire increases the PN prevalence estimate (framing effect), (ii) to investigate whether the health-related risk attitude is increased in students who use PN. Materials and methods: Two versions of a paper-and-pencil questionnaire (first version included caffeine tablets in the definition of PN, the second excluded caffeine tablets) were distributed among university students at the University of Graz, Austria. The unrelated question model (UQM) was used to estimate the 12-month PN prevalence and the German version of the 30-item Domain-Specific Risk-Taking (DOSPERT) scale to assess the health-related risk attitude. Moreover, large-sample z-tests (α = 0.05) were performed for comparing the PN prevalence estimates of two groups. Results: Two thousand four hundred and eighty-nine questionnaires were distributed and 2,284 (91.8%) questionnaires were included in analysis. The overall PN prevalence estimate for all students was 11.9%. One-tailed large-sample z-tests revealed that the PN estimate for students with higher health-related risk attitude was significantly higher compared to students with lower health-related risk attitude (15.6 vs. 8.5%; z = 2.65, p = 0.004). Furthermore, when caffeine tablets were included into the example of PN, the prevalence estimate of PN was significantly higher compared to the version without caffeine tablets (14.9 vs. 9.0%; z = 2.20, p = 0.014). Discussion: This study revealed that the PN prevalence estimate increases when caffeine tablets are included in the definition of PN. Therefore, future studies investigating the prevalence of, and predictors for, PN should be performed and interpreted with respect to potential framing effects. This study further revealed that the PN prevalence estimate is increased in students with a higher health-related risk attitude compared to students with a lower one. Therefore, future education and prevention programs addressing PN in the collective of students should not only inform about potential side effects of its use but also address the limited effects on cognition and potential alternatives of PN.

Bayesian Analysis: A Practical Approach to Interpret Clinical Trials and Create Clinical Practice Guidelines

Bayesian analysis is firmly grounded in the science of probability and has been increasingly supplementing or replacing traditional approaches based on P values. In this review, we present gradually more complex examples, along with programming code and data sets, to show how Bayesian analysis takes evidence from randomized clinical trials to update what is already known about specific treatments in cardiovascular medicine. In the example of revascularization choices for diabetic patients who have multivessel coronary artery disease, we combine the results of the FREEDOM trial (Future Revascularization Evaluation in Patients with Diabetes Mellitus: Optimal Management of Multivessel Disease) with prior probability distributions to show how strongly we should believe in the new Class I recommendation ("should be done") for a preference of bypass surgery over percutaneous coronary intervention. In the debate about the duration of dual antiplatelet therapy after drug-eluting stent implantation, we avoid a common pitfall in traditional meta-analysis and create a network of randomized clinical trials to compare outcomes after specific treatment durations. Although we find no credible increase in mortality, we affirm the tradeoff between increased bleeding and reduced myocardial infarctions with prolonged dual antiplatelet therapy, findings that support the new Class IIb recommendation ("may be considered") to extend dual antiplatelet therapy after drug-eluting stent implantation. In the decision between culprit artery-only and multivessel percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction, we use hierarchical meta-analysis to analyze evidence from observational studies and randomized clinical trials and find that the probability of all-cause mortality at longest follow-up is similar after both strategies, a finding that challenges the older ban against noninfarct-artery intervention during primary percutaneous coronary intervention. These examples illustrate how Bayesian analysis integrates new trial information with existing knowledge to reduce uncertainty and change attitudes about treatments in cardiovascular medicine.

[Monitoring the Paraguayan epidemiological dengue surveillance system (2009-2011) using Benford's law]

INTRODUCTION

Dengue is the most widespread arbovirus worldwide. In Paraguay, it reappeared in 1988-1989, with one of the largest epidemic outbreaks occurring in 2011.

OBJECTIVE

To evaluate the performance of the dengue epidemiological surveillance system in Paraguay between 2009 and 2011.

MATERIALS AND METHODS

We conducted an ecological study with secondary epidemiological surveillance data. We analyzed notified cases of the disease based on the distribution expected by Benford's law. To this end, we used the first and second digits from the global records stratified by region, season, population density, indicators of housing conditions and heads of cattle.

RESULTS

The epidemiological surveillance system performed better during non-epidemic periods and in the states with better housing conditions and fewer heads of cattle.

CONCLUSION

Given that a difference in the performance existed, we recommended that the system remains operating at the same high alert level even during periods when fewer cases are expected. The technology used by the method proposed to monitor the notification of cases is easy to transfer to operational staff.

Protein interaction network constructing based on text mining and reinforcement learning with application to prostate cancer

Constructing interaction network from biomedical texts is a very important and interesting work. The authors take advantage of text mining and reinforcement learning approaches to establish protein interaction network. Considering the high computational efficiency of co-occurrence-based interaction extraction approaches and high precision of linguistic patterns approaches, the authors propose an interaction extracting algorithm where they utilise frequently used linguistic patterns to extract the interactions from texts and then find out interactions from extended unprocessed texts under the basic idea of co-occurrence approach, meanwhile they discount the interaction extracted from extended texts. They put forward a reinforcement learning-based algorithm to establish a protein interaction network, where nodes represent proteins and edges denote interactions. During the evolutionary process, a node selects another node and the attained reward determines which predicted interaction should be reinforced. The topology of the network is updated by the agent until an optimal network is formed. They used texts downloaded from PubMed to construct a prostate cancer protein interaction network by the proposed methods. The results show that their method brought out pretty good matching rate. Network topology analysis results also demonstrate that the curves of node degree distribution, node degree probability and probability distribution of constructed network accord with those of the scale-free network well.

Criteria for reflex peripheral smear review in infants

Criteria for peripheral smear review are designed to include those samples with results outside the reference interval and can be more extreme based on what is considered to have clinical utility. However, we are unaware of previous studies that reported the distributions of various complete blood cell count (CBC) parameters in infants. In the following study we reviewed screening CBC results of 692 infants aged 9-15 months in order to determine the proportion of peripheral smear reviews recommended according to consensus criteria and that after adjusting for the observed distributions of the various parameters. According to consensus criteria the recommended reflex peripheral smear review rate was 39.7% (95% CI 36.1-43.4) whereas after adjustment for the observed distributions, the rate fell to 5.6% (95% CI 3.9-7.3) (p < 0.001). The major reasons for the difference in rates were the high proportion of infants with an absolute lymphocyte count > 7 × 10(9)/L (17.5%), the presence of a plus one blast flag (4.3%), and a large unstained cell count of ≥ 5% (26.2%) (equivalent to + 1 atypical flag). We found that international consensus criteria for reflex peripheral smear review results in a very high peripheral smear review rate in well infants, and might be inappropriate.

A statistical simulation model for field testing of non-target organisms in environmental risk assessment of genetically modified plants

Genetic modification of plants may result in unintended effects causing potentially adverse effects on the environment. A comparative safety assessment is therefore required by authorities, such as the European Food Safety Authority, in which the genetically modified plant is compared with its conventional counterpart. Part of the environmental risk assessment is a comparative field experiment in which the effect on non-target organisms is compared. Statistical analysis of such trials come in two flavors: difference testing and equivalence testing. It is important to know the statistical properties of these, for example, the power to detect environmental change of a given magnitude, before the start of an experiment. Such prospective power analysis can best be studied by means of a statistical simulation model. This paper describes a general framework for simulating data typically encountered in environmental risk assessment of genetically modified plants. The simulation model, available as Supplementary Material, can be used to generate count data having different statistical distributions possibly with excess-zeros. In addition the model employs completely randomized or randomized block experiments, can be used to simulate single or multiple trials across environments, enables genotype by environment interaction by adding random variety effects, and finally includes repeated measures in time following a constant, linear or quadratic pattern in time possibly with some form of autocorrelation. The model also allows to add a set of reference varieties to the GM plants and its comparator to assess the natural variation which can then be used to set limits of concern for equivalence testing. The different count distributions are described in some detail and some examples of how to use the simulation model to study various aspects, including a prospective power analysis, are provided.